# Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling

arXiv: https://arxiv.org/abs/2504.14130 · HTML: https://mlsanity.com/paper/2504.14130/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 19 Apr 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [DKN: Deep Knowledge-Aware Network for News Recommendation](https://mlsanity.com/paper/1801.08284/) (2018)
- [NPA: Neural News Recommendation with Personalized Attention](https://mlsanity.com/paper/1907.05559/) (2019)
- [Fastformer: Additive Attention Can Be All You Need](https://mlsanity.com/paper/2108.09084/) (2021)
- [Neural News Recommendation with Attentive Multi-View Learning](https://mlsanity.com/paper/1907.05576/) (2019)
- [Adversarial Personalized Ranking for Recommendation](https://mlsanity.com/paper/1808.03908/) (2018)
- [HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation](https://mlsanity.com/paper/2106.04408/) (2021)
- [Graph Enhanced Representation Learning for News Recommendation](https://mlsanity.com/paper/2003.14292/) (2020)
- [News Recommendation with Candidate-aware User Modeling](https://mlsanity.com/paper/2204.04726/) (2022)
- [Personalized News Recommendation with Knowledge-aware Interactive Matching](https://mlsanity.com/paper/2104.10083/) (2021)
- [FUM: Fine-grained and Fast User Modeling for News Recommendation](https://mlsanity.com/paper/2204.04727/) (2022)
- [Dual-space Hierarchical Learning for Goal-guided Conversational Recommendation](https://mlsanity.com/paper/2401.00272/) (2023)

## Built on by (most cited first, 1 of 1)

- [SenseJudge: Human-Centric Preference-Driven Judgment Framework](https://mlsanity.com/paper/2606.03189/) (2026)

## Abstract (arXiv)

Matching candidate news with user interests is crucial for personalized news recommendations. Most existing methods can represent a user's reading interests through a single profile based on clicked news, which may not fully capture the diversity of user interests. Although some approaches incorporate candidate news or topic information, they remain insufficient because they neglect the multi-granularity relatedness between candidate news and user interests. To address this, this study proposed a multi-granularity candidate-aware user modeling framework that integrated user interest features across various levels of granularity. It consisted of two main components: candidate news encoding and user modeling. A news textual information extractor and a knowledge-enhanced entity information extractor can capture candidate news features, and word-level, entity-level, and news-level candidate-aware mechanisms can provide a comprehensive representation of user interests. Extensive experiments on a real-world dataset demonstrated that the proposed model could significantly outperform baseline models.